Effect of TiO2 on preparation condition, mechanical properties and alkali resistance of continuous basalt fibers

Dou, Haoran; Bai, Jin; Lü, Hao; Zhang, Tingting; Kong, Lingxue; Bai, Zongqing; Li, Wen

doi:10.1016/j.cemconcomp.2022.104861

Cited by 16 publications

(3 citation statements)

References 34 publications

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“…It is believed that a smaller ratio of Fe 3+ /(Fe 2+ +Fe 3+ ) is beneficial for increasing the tensile strength of BCF 21,31 . Titanium‐rich basalt fibers commonly possess excellent mechanical properties, such as an increase in tensile strength of 60% when 0.25% TiO 2 is added, as the Ti 4+ connects the broken silica‐oxygen network by occupying the position of the missing Si 4+ and enhancing network aggregation 7,32 . Table 1 shows the recommended oxide composition from previous literature.…”

Section: Introductionmentioning

confidence: 99%

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

et al. 2023

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Tensile strength is the major mechanical property of basalt continuous fiber and is closely related to their chemical composition. This study constructed a machine learning model framework to predict the tensile strength of basalt continuous fiber. The database included the characteristic variables of oxides and their derived parameters, as well as the target variables of tensile strength. The mean squared error (MSE) were calculated to evaluate the performance of six machine learning models of decision tree, kernel ridge regression, multivariable linear regression, support vector regression, random forest, and k‐nearest neighbors. The k‐nearest neighbors model had a minimum MSE and was tuned using the grid search method with cross‐validation. The optimal hyper‐parameters of the k‐nearest neighbors model were K = 6, p = 10, and the determination coefficient of the model reached the maximum of 0.7110 on the test data. The limitations and implications of the present study were also demonstrated. We finally expected that the constructed framework could achieve higher prediction accuracy for tensile strength at a low cost in the future.Highlights A machine learning framework was built to predict the tensile strength of BCF. Oxides and their derived parameters were used for modeling. K‐nearest neighbors performed best in predicting the tensile strength. The framework was robust, cost‐saving, and efficient.

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Section: Introductionmentioning

confidence: 99%

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

et al. 2023

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show abstract

Section: Introductionmentioning

confidence: 99%

“…With respect to the formulation of glass compositions, the incorporation of rare earth or metal elements can obviously increase the resistance to alkali attack in fibers [ 6 , 9 ], such as zirconia and titanium dioxide, etc. Dou et al [ 9 ] investigated the effect of TiO 2 on the alkali resistance of basalt fibers in 1 mol/L NaOH solution. The incorporation of 0.25% TiO 2 by the mass of a basalt rock can form insoluble titanium hydroxide in the basalt fibers, which reduced the mass loss of the TiO 2 -basalt fibers at a corrosion age of 3 h by up to 50%.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Alkali Resistance of Glass Fibers via In Situ Modification of Manganese-Based Nanomaterials

Wang,

Zhang,

et al. 2023

Materials

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Glass fibers are widely used in cement-based precast products, given the reinforcing requirements for toughness and strength. However, inferior alkali resistance hinders the effectiveness of glass fibers in reinforcing cement-based materials. In this paper, nanoparticle coatings were applied on the surface of alkali-resistant glass fiber (ARGF) as a protective layer via the in situ chemical reaction of oleic acid (OA) and potassium permanganate (PP). The morphology and constituents of the as-prepared ARGFs were examined using scanning electron microscopy (SEM) and obtaining X-ray photoelectron spectroscopy (XPS) measurements. Mass loss and strength retention were investigated to characterize alkali resistance of modified ARGFs. Results showed that ARGFs could be optimally coated by a layer of MnO2-based nanoparticles consisting of approximately 70% MnO2, 18% MnO, and 12% MnSiO3, when modified with an optimum OA to PP ratio of 10 for 24 h. The dissolution of ARGFs matrix in 4% and 10% NaOH solutions were distinctly delayed to 28 d, as a consequence of the introduction of the MnO2-based nanoparticle layer, compared with nontreated ARGF occurring at 3 d in 4% NaOH solution. For the optimally modified ARGFs, the mass loss was controlled to 1.76% and 2.91% after 90 d of corrosion in 4% and 10% NaOH solutions, and the retention of tensile strength was increased by approximately 25%. With respect to the increment in alkali-resistant performance, the modified ARGFs can be promising candidates for wide applications in alkaline cement-based products.

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The Effect of Heat Treatment Methods on the Structural Characteristics of Basalt Glass

Zhang,

Wu,

Zeng

et al. 2024

Silicon

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Effect of TiO2 on preparation condition, mechanical properties and alkali resistance of continuous basalt fibers

Cited by 16 publications

References 34 publications

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

Enhancement of Alkali Resistance of Glass Fibers via In Situ Modification of Manganese-Based Nanomaterials

The Effect of Heat Treatment Methods on the Structural Characteristics of Basalt Glass

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